The current state of attribution modeling for display advertising is problematic for many applications. Standard approaches inadequately value inventory and intentionally disregard a large majority of information that could be used to improve an advertiser's return on its ad spend.
Attribution modeling refers to techniques that attribute correlation with or causality for a designated or defined event (sometimes referred to as a “conversion event”) to one or more preceding events or activities. In the field of online advertisement, for example, attribution modeling seeks to correlate or attribute causality for a conversion event (e.g., a user online transaction or download of a specific web page) to a past event (e.g., an online ad impression delivered to the user before the conversion event). An example of an attribution modeling technique in the field of online advertisement is the Last Touch Attribution Model (LTAM), which credits any downstream action (e.g., conversion event) with its most recent ad impression or click. This model is so simple and direct that a majority of information from most advertising campaigns is discarded in its use. Aside from being simple to compute, LTAM is also easy to explain, which contributes to its pervasiveness. However, LTAM ignores preceding events that may deserve partial credit for a conversion event.
Accordingly, there is a need for more sophisticated models to value impressions and other online activities or events that may relate to conversion events.